Multiple system atrophy (MSA) and Parkinson’s disease (PD) have clinical overlapping symptoms, which makes differential diagnosis difficult. Our research aimed to distinguish MSA from PD using corneal confocal microscopy (CCM), a noninvasive and objective test. The study included 63 PD patients, 30 MSA patients, and 31 healthy controls (HC). When recruiting PD and MSA, questionnaires were conducted on motor and non-motor functions, such as autonomic and cognitive functions. Participants underwent CCM to quantify the corneal nerve fibers. Corneal nerve fiber density (CNFD) and corneal nerve fiber length (CNFL) values in MSA are lower than PD (MSA vs. PD: CNFD, 20.68 ± 6.70 vs. 24.64 ± 6.43 no./mm², p < 0.05; CNFL, 12.01 ± 3.25 vs. 14.17 ± 3.52 no./mm², p < 0.05). In MSA + PD (combined), there is a negative correlation between CNFD and the Orthostatic Grading Scale (OGS) (r = −0.284, p = 0.007). Similarly, CNFD in the only MSA group was negatively correlated with the Unified Multiple System Atrophy Rating Scale I and II (r = −0.391, p = 0.044; r = −0.382, p = 0.049). CNFD and CNFL were inversely associated with MSA (CNFD: β = −0.071; OR, 0.932; 95% CI, 0.872 ~ 0.996; p = 0.038; CNFL: β = −0.135; OR, 0.874; 95% CI, 0.768–0.994; p = 0.040). Furthermore, we found the area under the receiver operating characteristic curve (ROC) of CNFL was the largest, 72.01%. The CCM could be an objective and sensitive biomarker to distinguish MSA from PD. It visually reflects a more severe degeneration in MSA compared to PD.

Objective: Prior research has underscored the importance of sphingolipid metabolism in Parkinson's disease (PD) pathogenesis. Our objective was to explore the associations between plasma ceramide levels and PD patients with cognitive dysfunction (PD‐CD).Methods: We enrolled two study populations from Eastern China and the Parkinson's Progression Markers Initiative (PPMI), comprising 290 (100 HCs, 160 PDs, and 30 MSAs) and 429 (125 HCs and 304 PDs) participants, respectively. The plasma levels of ceramides (Cer 16:0, Cer 18:0, Cer 24:0, and Cer 24:1) were tested via HPLC–MS/MS analysis.Results: Compared with those in the HC group, the plasma levels of Cer 18:0, Cer 24:1, Cer 16:0/Cer 24:0, Cer 18:0/Cer 24:0, and Cer 24:1/Cer 24:0 were higher in both the PD and MSA groups. Significant differences in the plasma levels of Cer 16:0/Cer 24:0, Cer 18:0/Cer 24:0, and Cer 24:1/Cer 24:0 were observed among the PD‐NC (PD with normal cognition), PD‐MCI (PD with mild cognitive impairment), and PDD (PD dementia) groups, with the PDD group exhibiting the highest levels. PD patients with higher baseline levels of plasma ceramides (specifically, Cer 18:0, Cer 16:0/Cer 24:0, Cer 18:0/Cer 24:0, and Cer 24:1/Cer 24:0) demonstrated accelerated cognitive decline compared with individuals who had lower baseline plasma ceramide levels during the 5‐year follow‐up period. A biomarker panel including Cer 18:0/Cer 24:0 and Cer 24:1/Cer 24:0 could effectively differentiate PD‐CD from PD‐NC with notable diagnostic accuracy.Conclusions: Our results indicate that plasma ceramide levels could potentially be used as diagnostic biomarkers for PD‐CD.

Objectives: The objective of this study is to investigate the indirect causalities between gut microbiota and sleep disorders.Methods: In stage 1, we utilized 196 gut microbiota as the exposure factor and conducted a two-sample univariable Mendelian randomization (MR) analysis on five sleep disorders: insomnia, excessive daytime sleepiness (EDS), sleep-wake rhythm disorders (SWRD), obstructive sleep apnea (OSA), and isolated REM sleep behavior disorder (iRBD). In stage 2, we validated the MR findings by comparing fecal microbiota abundance between patients and healthy controls through 16S rDNA sequencing. In stage 3, we explored the indirect pathways by which the microbiota affects sleep, using 205 gut microbiota metabolic pathways and 9 common risk factors for sleep disorders as candidate mediators in a network MR analysis.Results: In stage 1, the univariable MR analysis identified 14 microbiota potentially influencing five different sleep disorders. In stage 2, the results from our observational study validated four of these associations. In stage 3, the network MR analysis revealed that the Negativicutes class and Selenomonadales order might worsen insomnia by increasing pain [mediation: 12.43% (95% CI: 0.47, 24.39%)]. Oxalobacter could raise EDS by disrupting adenosine reuptake [25.39% (1.84, 48.95%)]. Allisonella may elevate OSA risk via obesity promotion [36.88% (17.23, 56.54%)], while the Eubacterium xylanophilum group may lower OSA risk by decreasing smoking behavior [7.70% (0.66, 14.74%)].Conclusion: Triangulation of evidence from the MR and observational study revealed indirect causal relationships between the microbiota and sleep disorders, offering fresh perspectives on how gut microbiota modulate sleep.